Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "246" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 17 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 17 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459994 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.210357 | 14.679000 | -0.761695 | 4.862047 | -0.657047 | 9.550857 | -0.608567 | -0.281128 | 0.5670 | 0.0383 | 0.4789 | nan | nan |
| 2459991 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.244357 | 17.563898 | -0.104190 | 4.008853 | -0.743029 | 10.754274 | -0.747046 | -0.258417 | 0.5685 | 0.0378 | 0.4851 | nan | nan |
| 2459990 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.232110 | 14.466029 | -0.121534 | 3.763996 | -0.484311 | 11.001251 | -1.059471 | -0.628874 | 0.5657 | 0.0406 | 0.4775 | nan | nan |
| 2459989 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.056263 | 14.703069 | 0.096473 | 3.582439 | -0.776886 | 9.205380 | -0.929247 | -0.762043 | 0.5630 | 0.0374 | 0.4806 | nan | nan |
| 2459988 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.088960 | 17.191859 | -0.144920 | 3.805519 | -0.835547 | 13.186218 | -0.784190 | -0.580267 | 0.5640 | 0.0371 | 0.4861 | nan | nan |
| 2459987 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.189818 | 14.032342 | -0.704281 | 4.598712 | -0.704093 | 7.987850 | -0.523968 | 0.251619 | 0.5740 | 0.0395 | 0.4900 | nan | nan |
| 2459986 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.165956 | 17.195367 | -0.750927 | 4.877187 | -0.888642 | 11.267779 | -1.068745 | 8.815457 | 0.6017 | 0.0384 | 0.5188 | nan | nan |
| 2459985 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.174971 | 15.563391 | -0.795471 | 4.625803 | -0.885920 | 8.628127 | -0.028432 | 0.028804 | 0.5754 | 0.0384 | 0.4950 | nan | nan |
| 2459984 | dish_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | -0.325397 | 14.910039 | -0.667381 | 4.915423 | -0.111494 | 12.087065 | -0.608584 | 1.995096 | 0.5884 | 0.0407 | 0.5142 | nan | nan |
| 2459983 | dish_maintenance | 100.00% | 0.00% | 88.82% | 0.00% | - | - | 0.482784 | 10.973549 | -0.750840 | 3.054233 | -0.672682 | 82.061210 | -0.769824 | 3.835909 | 0.5757 | 0.1299 | 0.4397 | nan | nan |
| 2459982 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 25.157412 | 23.383106 | -0.406651 | -0.512077 | 2.192807 | 2.116051 | 0.619122 | 1.469260 | 0.3764 | 0.3646 | 0.1242 | nan | nan |
| 2459981 | dish_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.473594 | 6.982003 | -1.032840 | -0.118599 | 5.213998 | 5.193791 | 3.447987 | -0.607713 | 0.3203 | 0.3128 | 0.1525 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 7.956991 | 6.919268 | -1.173942 | -0.597849 | 5.112120 | 4.367627 | 2.841944 | 2.699395 | 0.3837 | 0.3746 | 0.1398 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.663753 | 6.740278 | -0.593848 | -0.002752 | 4.453238 | 4.277173 | 3.191473 | -0.612220 | 0.3127 | 0.3078 | 0.1551 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.965991 | 7.243448 | -0.539978 | 0.177313 | 4.371044 | 4.862858 | 3.847042 | -0.749290 | 0.3092 | 0.3066 | 0.1560 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.821914 | 7.138965 | -1.109622 | -0.612569 | 8.673340 | 5.938070 | 1.686417 | -0.668457 | 0.2963 | 0.2915 | 0.1319 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 8.650674 | 7.491857 | -1.165839 | -0.470195 | 4.000917 | 4.043417 | 1.881292 | 0.022232 | 0.3161 | 0.3142 | 0.1538 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 14.679000 | -0.210357 | 14.679000 | -0.761695 | 4.862047 | -0.657047 | 9.550857 | -0.608567 | -0.281128 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 17.563898 | 0.244357 | 17.563898 | -0.104190 | 4.008853 | -0.743029 | 10.754274 | -0.747046 | -0.258417 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 14.466029 | 14.466029 | 0.232110 | 3.763996 | -0.121534 | 11.001251 | -0.484311 | -0.628874 | -1.059471 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 14.703069 | 14.703069 | 0.056263 | 3.582439 | 0.096473 | 9.205380 | -0.776886 | -0.762043 | -0.929247 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 17.191859 | 17.191859 | 0.088960 | 3.805519 | -0.144920 | 13.186218 | -0.835547 | -0.580267 | -0.784190 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 14.032342 | -0.189818 | 14.032342 | -0.704281 | 4.598712 | -0.704093 | 7.987850 | -0.523968 | 0.251619 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 17.195367 | 17.195367 | 0.165956 | 4.877187 | -0.750927 | 11.267779 | -0.888642 | 8.815457 | -1.068745 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 15.563391 | 15.563391 | -0.174971 | 4.625803 | -0.795471 | 8.628127 | -0.885920 | 0.028804 | -0.028432 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Shape | 14.910039 | -0.325397 | 14.910039 | -0.667381 | 4.915423 | -0.111494 | 12.087065 | -0.608584 | 1.995096 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | nn Temporal Variability | 82.061210 | 0.482784 | 10.973549 | -0.750840 | 3.054233 | -0.672682 | 82.061210 | -0.769824 | 3.835909 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | ee Shape | 25.157412 | 25.157412 | 23.383106 | -0.406651 | -0.512077 | 2.192807 | 2.116051 | 0.619122 | 1.469260 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | dish_maintenance | ee Shape | 8.473594 | 6.982003 | 8.473594 | -0.118599 | -1.032840 | 5.193791 | 5.213998 | -0.607713 | 3.447987 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | RF_maintenance | ee Shape | 7.956991 | 6.919268 | 7.956991 | -0.597849 | -1.173942 | 4.367627 | 5.112120 | 2.699395 | 2.841944 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | RF_maintenance | ee Shape | 8.663753 | 8.663753 | 6.740278 | -0.593848 | -0.002752 | 4.453238 | 4.277173 | 3.191473 | -0.612220 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | RF_maintenance | ee Shape | 8.965991 | 7.243448 | 8.965991 | 0.177313 | -0.539978 | 4.862858 | 4.371044 | -0.749290 | 3.847042 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | RF_maintenance | ee Shape | 8.821914 | 8.821914 | 7.138965 | -1.109622 | -0.612569 | 8.673340 | 5.938070 | 1.686417 | -0.668457 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 246 | N20 | RF_maintenance | ee Shape | 8.650674 | 7.491857 | 8.650674 | -0.470195 | -1.165839 | 4.043417 | 4.000917 | 0.022232 | 1.881292 |